Document-level relation extraction is a challenging task which requires
reasoning over multiple sentences in order to predict relations in a document.
In this paper, we pro-pose a joint training frameworkE2GRE(Entity and Evidence
Guided Relation Extraction)for this task. First, we introduce entity-guided
sequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa). These
entity-guided sequences help a pre-trained language model (LM) to focus on
areas of the document related to the entity. Secondly, we guide the fine-tuning
of the pre-trained language model by using its internal attention probabilities
as additional features for evidence prediction.Our new approach encourages the
pre-trained language model to focus on the entities and supporting/evidence
sentences. We evaluate our E2GRE approach on DocRED, a recently released
large-scale dataset for relation extraction. Our approach is able to achieve
state-of-the-art results on the public leaderboard across all metrics, showing
that our E2GRE is both effective and synergistic on relation extraction and
evidence prediction.

这篇论文提出了一种基于预训练语言模型的 E2GRE 框架，通过实体引导序列和内部注意概率作为额外特征，来帮助文献级关系抽取和证据预测，并取得了在 DocRED 数据集上最先进的结果。